A Video Summarization Technique using Multi- Feature DWHT and GMM for CBVR System

Abstract

The increasing utilization of multimedia data and digital information in present times presents a vast scope for research in content-based retrieval systems. An improved CBVR System is proposed to extract video streams effectively using DWHT Multi- features and GMM. Our CVBR method performs VSBD for identifying Video shots by computing DWHT on video frames for multi- feature extraction, and then key frames are identified. A summarized frame is developed using the VS algorithm based on GMM on the UCF Dataset. Later, a procedure is applied for the input query video stream, and correlation coefficients are calculated between the query and the database multi-feature vectors, giving us similarity measures. Lastly, our experimental results validate the efficiency of our proposed CBVR System, achieving an average precision of 0.821 and a loss of 0.179, outperforming existing CBVR systems using DCT and optimized perceptual VS, which have precision values of 0.6475 and 0.71, respectively, along with losses of 0.3525 and 0.29.